Sr. Applied Scientist, Knowledge Tech

- PhD, or Master's degree and 6+ years of applied research experience
- 3+ years of building machine learning models for business application experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
Amazon’s builders are able to deliver the magic that gets packages to your front door and safely scale those capabilities to millions of customers because they can find the information they need. Amazon is looking for an Applied Scientist to help drive our discovery strategy for Amazon’s trove of knowledge.

Key job responsibilities
- Use deep learning, ML and NLP techniques to create scalable solutions for creation and development of language model centric solutions for building personalized assistant systems based on a rich set of structured and unstructured contextual signals
- Innovate new methods for contextual knowledge extraction and information retrieval, using language models in combination with other learning techniques, that allows effective grounding in context providers when considering memory, compute, latency and quality
- Research in advanced customer understanding and behavior modeling techniques
- Collaborate with cross-functional teams of engineers, product managers, and scientists to identify and solve complex problems in personal knowledge aggregation, processing, modeling, and verification
- Design and execute experiments to evaluate the performance of state-of-the-art algorithms and models, and iterate quickly to improve results
- Think Big about the arc of development of conversational assistant system personalization over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems
- Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports
- Mentor and guide junior scientists and engineers, and contribute to the overall growth and development of the team

A day in the life
The Amazon Software Builder Experience (ASBX) organization has a mission of creating the world’s best builder experience across tens of thousands of software engineers, and all Amazon businesses (e.g., AWS, Amazon Stores, etc.). Our Knowledge Tech team in ASBX owns the discovery tools that software developers use to build and innovate on behalf of our customers. This role is for you if you’re passionate about software development, discovery tools, and having world-wide impact across all of Amazon! In addition to delivering results, we also appreciate establishing a work-life-balance, having fun with our co-workers, and supporting an inclusive and collaborative team environment.